Systems and methods for neural bridging of the nervous system

ABSTRACT

The present disclosure relates generally to systems, methods, and devices for interpreting neural signals to determine a desired movement of a target, transmitting electrical signals to the target, and dynamically monitoring subsequent neural signals or movement of the target to change the signal being delivered if necessary, so that the desired movement is achieved. In particular, the neural signals are decoded using a feature extractor, decoder(s) and a body state observer to determine the electrical signals that should be sent.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application a 371 of PCT Patent Application Serial No.PCT/US2016/035524, filed Jun. 2, 2016, which claims priority to U.S.Provisional Patent Application Ser. No. 62/169,820, filed Jun. 2, 2015.These disclosures are fully incorporated by reference herein.

BACKGROUND

The present disclosure relates generally to systems, methods and devicesfor neuromuscular stimulation by interpretation of neural activity andprocessing to identify desired movements of a target, and provideappropriate stimulation to the target to cause the desired movements.

Many millions of people suffer from some motor impairment. For example,it is estimated that worldwide, 10 million people are left disabledfollowing a stroke each year. People also suffer from brain injury,failed back surgery or spinal cord injury. These injuries can result inmotor impairment by damaging the link between the brain and the musclesof the body which are used for movement. For example, neurons in thebrain may die, or the nerves between the brain and the muscle aresevered. These disrupt the paths by which electrical signals travel fromthe brain to neuromuscular groups to effectuate coordinated musclecontraction patterns.

Neuromuscular stimulation devices have been used to deliver stimulationto restore movement to parts of the body not under volitional control.For example, subcutaneous implantable neurostimulation cuffs have beenused to block pain and to restore function to damaged or degenerativeneural pathways. These implantable cuffs are wrapped around a targetnerve and generally include one or more electrodes arranged to stimulatethe nerve.

Transcutaneous neurostimulation cuffs behave similarly to implantablecuffs, however there are important differences. Because the electrodesare placed on the surface of the skin, rather than below it, stimulationoften can better target skeletal muscle tissue or muscle groups, ratherthan peripheral nerves located deeper under the skin. Muscularstimulation may be preferable to stimulating major peripheral nerves,e.g. ulnar, median, radial nerves, as stimulating these nerves may causea patient to feel a tingling sensation and it is more difficult toeffect the desired movement. By increasing the number and layout ofelectrodes in a neuromuscular cuff, similar to the direction taken withimplanted nerve cuff designs, current generation neuromuscularstimulation cuffs have been able to selectively stimulate individualmuscles or muscle groups and achieve finer movements such as individualfinger flexing and extension.

Flexible-like transcutaneous cuffs have been developed which fit arounda human appendage such as a forearm to control the wrist or fingers.These flexible cuffs may include sensors which record muscle activity,or electromyography (EMG) signals, and stimulate in response to the EMGsignals.

Neurostimulation devices do not, by themselves, resolve the problem ofmotor impairment due to neural damage, because such a device by itselfdoes not respond to volitional control. For example, the user of aneurostimulation device cannot cause the device to stimulate muscleactivity by willing such muscle activity to occur. An effective systemfor translating human brain signals into desired movements in real timeand sending a signal to cause the desired movements has not yet beendeveloped. It would be desirable to provide such systems and methodssuch that a neural signal indicative of an intended action couldeffectuate the intended action, either in a damaged neuromuscular regionor in an electronic device.

BRIEF SUMMARY

The present disclosure relates to systems and methods for recognizingdesired movements from the neural signals of a user in real time, andtranslating those desired movements into electrical signals to be sentto a target, which can be the user's limb, a prosthetic, or some otherelectronic device. Thus, it is possible for a chronically paralyzed userto regain movement through the decoding of intracortical signalscaptured by a brain implant and subsequent routing of those messages tothe muscles to accomplish the desired movement.

Various embodiments of continuous methods of creating a neural bridgeare disclosed within, which include: measuring a first neural activityof a patient; based on the first measurement of neural activity,determining a desired movement of a target; delivering an electricalsignal to the target to start movement of the target and measuring asecond neural activity of the patient; and based on the second neuralactivity of the patient or motion of the target, maintaining or changingthe signal being delivered to the target as the target moves, so thatthe desired movement is achieved.

The methods may further include removing at least one artifact from themeasurement of neural activity prior to determining the desired movementof the target. Determining the desired movement may comprise extractingat least one feature from the measured neural activity. The extractedfeature may be a signal amplitude, amplitude of the signal in a givenfrequency range, amplitude of the signal in a wavelet scale, or a firingrate. The determining may further include feeding the at least oneextracted feature to a decoder to identify additional features in ahigher dimensional space. The decoder may look for patterns in thefeatures that indicate the desired movement. The determining of thedesired movement may further include sending the features to both adiscrete multiclass decoder and a movement effort decoder, wherein thediscrete multiclass decoder determines the desired movement and themovement effort decoder determines a level of movement effort by thepatient; and using outputs from the discrete multiclass decoder and themovement effort decoder as inputs for forming the signal delivered tothe target.

The outputs from the discrete multiclass decoder and the movement effortdecoder can be nonlinearly modified to determine the signal delivered tothe target. The subsequent neural activity or the motion of the targetcan be used to determine a body state of the target, and the signal maybe dynamically changed based on the determined body state. Sensor inputmay be used to determine the body state of the target. A history offeedback provided to the patient may also be used to determine the bodystate of the target. A patient activation profile may also be used todetermine the signal sent to the target. The signal delivered to thetarget may be a multiplexed signal that includes multiple interleavedsignals that do not interfere with each other. The target may be a bodylimb or a prosthetic limb, a wheelchair, a cursor on a computer, anexoskeleton, a remote control device, or an external robotic arm.

Also disclosed are methods of creating a neural bridge, including:measuring neural activity of a patient; decoding a desired movement of atarget based on the measurement of neural activity; and delivering amultiplexed signal to the target, the multiplexed signal containingmultiple interleaved signals that do not interfere with each other andproduce the desired movement of the target.

Still other additional methods disclosed herein for creating a neuralbridge include: measuring neural activity of a patient; determining adesired movement of a target based on the measurement of neuralactivity; identifying actions about multiple joints of the target thatwill result in the desired compound movement, and identifying actuatingsignals to the multiple joints that do not interfere with each other andthat will result in the desired compound movement; and delivering amultiplexed signal to the target, the multiplexed signal containing themultiple actuating signals interleaved together, to produce the desiredcompound movement of the target.

The methods used herein can be used to decode desired movements in realtime and use real-time feedback to improve the motion of the target.Compound movements (e.g. simultaneous movement of multiple joints) canalso be attained from a single stimulation pattern. Neural signals canbe directly processed and then sent to the target to “bridge” any gapsin the normal nerve signal transmission pathways between the brain andthe limb whose movement is desired. The systems and methods disclosedherein are particularly advantageous in the treatment of spinal cordinjury, stroke, multiple sclerosis, motor neuron disease, and traumaticbrain injury. Other advantages will become apparent to one of ordinaryskill in the art upon reading and understanding this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The following is a brief description of the drawings, which arepresented for the purposes of illustrating the exemplary embodimentsdisclosed herein and not for the purposes of limiting the same.

FIG. 1 is a diagram illustrating the methods of the present disclosurefor decoding a neural signal to determine the desired movements, and anartificial neuromuscular stimulation system with volitional control forimplementing those desired movements.

FIG. 2 illustrates plots of a neural signal as a function of time beforeand after artifact removal. In the “before” graph at top, the y-axis isvoltage in microvolts, with the scale ranging from −4000 to +10,000 μVat intervals of 2000 μV. The x-axis is time, with the scale ranging from0 milliseconds (msec) to 100 milliseconds at intervals of 10 msec. Inthe “after” graph at bottom, the y-axis is voltage in microvolts, withthe scale ranging from −400 to +400 μV at intervals of 100 μV. Thex-axis is time, with the scale ranging from 0 milliseconds (msec) to 100milliseconds at intervals of 10 msec. It can be seen that the “before”neural signal versus time dataset has been processed such that the“after” neural signal versus time dataset is of a shorter duration(about 10% shorter), which is due to removal of signal artifacts.

FIG. 3A is a diagram indicating single-motion decoders, which providesimple binary output (yes/no) to identify a desired movement and aresuitable illustrative embodiments of the decoder(s) of the system ofFIG. 1.

FIG. 3B shows a discrete multiclass decoder and a movement effortdecoder, which is another suitable illustrative embodiment of thedecoder(s) of the system of FIG. 1. The multiclass decoder can beconsidered an amalgamation of the single-motion decoders illustrated inFIG. 3A. The movement effort decoder outputs a measure of the focuslevel being applied to attain the desired movement.

FIG. 4 is a diagram illustrating additional features of the methodsdescribed herein, also including boxes showing how the decoders aretrained to identify desired movements from a neural signal.

FIG. 5 shows an illustrative graphical user interface display depictinga human hand, which may be shown on the computer display of the systemof FIG. 1.

FIG. 6 is a flowchart of an exemplary neuromuscular stimulation methodsuitably performed in conjunction with the system of FIG. 1, whichincludes corrective feedback.

FIGS. 7A-18B are pictures of several different hand and arm motionsobtained from an experiment in which an able-bodied user was asked tomove his hand/arm into a given position. In each set, one picture showsan image of the hand/arm motion that the user performed with his limb,and the other picture shows a graphical hand representation of theuser's limb position based on position sensor data collected from thesleeve. In each set of pictures, the actual limb moved is shown as theleft hand, and the graphical limb moved was the right hand.

FIG. 7A shows the limb moved for hand pronation, palm down, and FIG. 7Bshows the measured position in a graphical limb.

FIG. 8A shows the limb moved for perpendicular arm lift, and FIG. 8Bshows the measured position in a graphical limb.

FIG. 9A shows the limb moved for hand neutral position, and FIG. 9Bshows the measured position in a graphical limb.

FIG. 10A shows the limb moved for hand pronation, thumb down, and FIG.10B shows the measured position in a graphical limb.

FIG. 11A shows the limb moved for supination, palm up, and FIG. 11Bshows the measured position in a graphical limb.

FIG. 12A shows the limb moved for wrist extension, and FIG. 12B showsthe measured position in a graphical limb.

FIG. 13A shows the limb moved for wrist flexion, and FIG. 13B shows themeasured position in a graphical limb.

FIG. 14A shows the limb moved for radial deviation, and FIG. 14B showsthe measured position in a graphical limb.

FIG. 15A shows the limb moved for ulnar deviation, and FIG. 15B showsthe measured position in a graphical limb.

FIG. 16A shows the limb moved for finger extension, and FIG. 16B showsthe measured position in a graphical limb.

FIG. 17A shows the limb moved for finger fair curvature, and FIG. 17Bshows the measured position in a graphical limb.

FIG. 18A shows the limb moved for closing the fingers into a fist, andFIG. 18B shows the measured position in a graphical limb.

FIGS. 19A-19C are bar graphs showing the different percentages ofcurvature for each digit during finger extension, finger fair curvature,and making a fist. For each graph, 0 corresponds to the thumb, 1corresponds to the index finger, 2 corresponds to the middle finger, 3corresponds to the ring finger, and 4 corresponds to the pinky finger.The y-axis is the finger curvature percentage, and runs from 0% to 100%in intervals of 20%. FIG. 19A shows the percentages for fingerextension. FIG. 19B shows the percentages for finger fair curvature.FIG. 19C shows the percentages when a first is made.

FIG. 20 is an illustration of a two-neuron oscillator based on theMatsuoka neural model, which was used to simulate a central patterngenerator (CPG).

FIG. 21 is a picture of a hand with a “sixth” finger that served as areference point for measurement during testing.

FIGS. 22A-22C are sets of graphs. In each figure, the top graph is araster histogram from one discriminated unit (channel 7 unit 1) showingthe response to attempted thumb movement. Six trials are shown, and eachdot in the raster represents a spike. The bottom graph is waveletprocessed neural data from channel 7 showing the mean wavelet power(MWP) (dark line), with a confidence interval of ±1 standard deviationaround the mean. FIG. 22A is for thumb flexion. FIG. 22B is for thumbextension. FIG. 22C is for thumb wiggle.

FIGS. 22A-22C are sets of graphs showing differences in neural activitybetween electrode channels for three movements: CPG thumb wiggle, thumbextension, and thumb flexion. FIG. 23A shows the average normalized meanwavelet power for each electrode in the 96-electrode array for the threedifferent movements. The power is in units of standard deviations awayfrom a baseline non-movement period of rest.

FIG. 23B is a graph showing the average correlation coefficient betweenthe neural activity and the cue vector for each channel, and shows thechange in modulation levels on certain channels between the threemovements.

FIG. 23C is a set of graphs showing that channels 7, 70, and 87 showdistinct differences in neural modulation when comparing thumb wiggle tochumb flexion and extension. The x-axis is the time in seconds when acue was given to imagine the noted movement, and the y-axis is themodulation in standard deviations away from the baseline.

FIGS. 24A-24C are sets of graphs. In each figure, the top graph is aheat map of the mean wavelet power (MWP) before stimulation and afterstimulation. The y-axis is the channel, and the x-axis is the time(secs) when the signal is detected. The white line marks when thestimulation is turned on. The MWP is in units of standard deviation awayfrom a baseline non-movement period. In the bottom graph, the dottedline is the neural decoder output score, and the solid line is thephysical thumb movement. Only decoder outputs greater than zero areshown for clarity. FIG. 24A is for CPG thumb wiggle. FIG. 24B is forthumb flexion. FIG. 24C is for thumb extension.

DETAILED DESCRIPTION

A more complete understanding of the methods and apparatuses disclosedherein can be obtained by reference to the accompanying drawings. Thesefigures are merely schematic representations and are not intended toindicate relative size and dimensions of the assemblies or componentsthereof.

Although specific terms are used in the following description for thesake of clarity, these terms are intended to refer only to theparticular structure of the embodiments selected for illustration in thedrawings, and are not intended to limit the scope of the disclosure. Inthe drawings and the following description below, it is to be understoodthat like numeric designations refer to components of like function.

The present disclosure relates to methods for providing an artificialneuromuscular stimulation system with volitional control, e.g. bydecoding neural activity to determine a desired movement, then transmitthe desired movement to a target to perform the desired movement. Forexample, these methods would be useful for patients who may havesuffered nerve injury to bypass that disruption and “bridge” that gap tosend electrical signals to a body limb, for example an arm or leg.Desirably, it would be as if the disruption did not exist; the patientwould simply think of the desired movement of the body limb, and thatmovement would occur. In other embodiments, rather than being a bodylimb, electrical signals may be delivered to a target that is non-human(e.g. an electronic device). This allows for control of the non-humantarget by use of the electrical signals. Examples of non-human targetsthat may be controlled in this way include: a prosthetic limb, awheelchair, a cursor on a computer, an exoskeleton, a remote controldevice, and an external robotic arm.

Referring first to FIG. 1, an artificial neurostimulation systemprovides artificial neurostimulation to a human user 150 undervolitional control of the subject 150. The artificial neurostimulationsystem includes a transcutaneous neurostimulation sleeve 152 forproviding electrical stimulation to a body part of the subject 150, anda computer or other electronic data processing device 154 connected toreceive neural signals from neural sensors, and to output controlsignals to the neurostimulation sleeve, and further programmed todetermine a volitional intent of the subject based on the receivedneural signals and to generate the output control signals to implementthat volitional intent. The user 150 is assumed to have motor impairmentdue to neural damage, such that volitional control, that is, simplythinking or intending of a desired motion (e.g. intending to fold twofingers against the palm) does not result in the desired motion. Thiscan occur due to nerve injury between the brain and the fingers.Instead, a computer 154 receives the user's neural activity as an input,and determines the desired motion. The computer then generates anelectrical stimulation pattern which is delivered to a neurostimulationsleeve 152, which is worn by the patient on the arm and which includeselectrodes. The electrical stimulation of the sleeve 152 results in thedesired bending of fingers. The system provides an artificial neuralbypass, or neural “bridge”, around the injury to provide control of thefingers.

FIG. 1 also illustrates a flowchart/algorithm for determining thedesired motion from a user's neural activity and translating that intoan electrical stimulation pattern that can be transmitted. First, theneural activity of the user 150 is measured in operation 100 to obtain aneural signal. Noise artifacts are then removed from the neural signalin operation 102. To determine a desired movement, one or more featuresare extracted from the measured neural signal in operation 104.Subsequently, the extracted feature(s) are sent to decoders in operation106. The decoders determine the desired movement. That output is thensent to a body state observer 108, which is a model of the various partsof the user's body and the target. Inputs from body movement sensors 110may also be taken into account in the body state observer 108, which isused to predict future movements that need to be made to obtain thedesired movement. In operation 112, high definition stimulation controlis used to calculate the stimulation pattern that is needed to obtainthe desired movement. A user activation profile 114 containinginformation on the user's reaction to various stimulation patterns canbe used here to customize the resulting stimulation pattern/signal thatis determined. That stimulation pattern is then sent to stimulationelectrodes 118, which stimulate the appropriate part of the target (e.g.muscle groups, motors, etc.). Alternatively, during training, the systemcalculates the effect such stimulation would have, and displays this inthe form of a graphical body part 116, to provide feedback to the user.This algorithm repeats at a high rate, permitting continuous real-timeupdating of the stimulation signal to the target. Thus, it should beunderstood that the methods described herein may be performed eithercontinuously or intermittently.

The measurement of neural activity 100 can be done by any suitabletechnique. For example, electroencephalography (EEG), a noninvasivetechnique, may be used. In EEG, electrodes are placed on a scalp of user150 to measure electrical activity along the scalp. Invasive techniques(electrodes placed beneath the scalp) are also contemplated for use. Forexample, a “Utah array” of electrodes, such as that made by BlackrockMicrosystems, may be used. The Utah array can have up to 96 electrodes.Also contemplated is the use of a “Michigan array” of electrodes, suchas that made by NeuroNexus. Such microelectrode arrays would beimplanted into the brain, for example in the primary motor cortex.Electrocorticography (ECoG) may also be used. In ECoG, electrodes areplaced directly on the exposed surface of the brain to record electricalactivity. The location in which the electrodes are implanted can dependon the location of the injury. An appropriate location for implantationcan be determined by showing a video of able-bodied movement of therelevant body part to the patient/user while performing functionalmagnetic resonance imaging (fMRI).

These electrode arrays record “brain waves,” more particularly neuralsignals which are representative of a varied set of mental activities.Neural signals include electrical signals produced by neural activity inthe nervous system including action potentials, multi-unit activity,local field potential, ECoG, and EEG. The neural signals from eachelectrode can be sampled at rates of at least 10 kHz or 30 kHz,providing a robust stream of data (though not all of this data needs tobe used). These neural signals are sent wirelessly or, alternatively,through a wired connection, to a neural signal processing device forprocessing of the neural signals.

Next, the neural signal is processed 102 to obtain a “clean” signal. Inthis regard, for most purposes, it is desirable for each electrode torecord the signal from a given neuron, rather than a set of givenneurons. The brain is very busy electrically, and the presence of otherneurons in the vicinity of these delicate and sensitive electrodes cancreate noise that obscures the desired signal. The signals actuallydetected by the electrode array are first amplified and then filtered toremove noise outside of a frequency band of interest (e.g. 0.3 Hz to 7.5kHz). The signal may be processed in analog or digital form. Examples ofuseful analog filters include an 0.3 Hz 1st order high pass filter and a7.5 kHz 3rd order low pass filter. An example of a digital filter is adigital 2.5 kHz 4th order Butterworth low pass filter. Processing mayoccur at desirable rates, for example every 100 milliseconds.

Part of the processing 102 involves artifact removal. Artifact removalis used to “clean up” the neural activity data and results in improvedprocessing. Artifacts in the data may have been caused by, for example,electrical stimulation in the body limb (e.g. forearm) whose movement isdesired. FIG. 2 shows a signal before artifact removal 200, and showsthe “same” signal after artifact removal 202. Identification of anartifact may be accomplished by, for example, by detecting a thresholdcrossing that occurs at the same time on a large fraction of thechannels in the signal data. Here, for example, the artifacts are theextremely high peaks up to 8000 μV at periodic rates of −20 msec. Thethreshold can be fixed or dynamically calculated, and for example can bemodified based on factors already known to the processor such as whenelectrical stimulation is delivered through the neurostimulation sleeve.A set time window of data may then be removed around the detectedartifact. The data is then realigned (e.g. voltage-wise) and thenstitched back together (e.g. time-wise). For example, as shown here, thetime window is 2.5 milliseconds, based on the system's recovery periodfor an amplifier. The five peaks are thus removed, and the remainingsignal is shown on the bottom. As seen here, once the artifacts areremoved, the signal is of much smaller magnitude but contains usefulinformation.

Of course, when data is being measured on multiple channels (e.g. from a96-channel microelectrode array), the artifact should be removed on eachchannel. One common method of artifact removal is to determine theaverage from all or most of the channels, and then subtract the averagefrom each channel. Alternatively, the stimulation signal can be shapedin such a way that artifacts on certain frequencies may be prevented orreduced. In other embodiments, the artifacts can be planned somewhat.For example, the electrical stimulation delivered through theneurostimulation sleeve 152 could have a known shape, such as a squarepulse. These will aid in removing artifacts from the neural signal.

Next, in operation 104 of FIG. 1, features are extracted from the neuralsignal. “Feature” is the term given to various pieces of data, eitherfrom each electrode individually or some/all electrodes considered as agroup, that can contain useful information for determining the desiredmovement. Examples of such features include: signal amplitude, amplitudeof the signal in a given frequency range, amplitude of the signal in awavelet scale, or a firing rate. Here, a firing rate may refer to thenumber of action potentials per unit of time for a single neuron. Again,the extracted features provide useful information about the neuralnetwork being monitored. Desirably, the feature has a high signal-tonoise ratio, preferably 2 or greater, and more preferably 3 or greater.

One particular set of features is obtained by applying a waveletdecomposition to the neural signals, using the ‘db4’ wavelet and 11wavelet scales, as described in Mallat, S. G., A Wavelet Tour of SignalProcessing, Academic Press (1998), (ISBN 9780080922027). Scales 3 to 6are used to approximate the power in the multi-unit frequency bandspanning approximately 117 Hz to 1875 Hz. The mean of waveletcoefficients for each scale of each channel (of the 96-electrode array)is taken across 100 milliseconds of data. A 15-second sliding window(e.g. boxcar filter) is used to calculate the running mean of eachwavelet scale for each channel. The 15-second mean is then subtractedoff of the mean wavelet coefficients of the current 100 ms window. Themean subtraction is used in order to account for drift in the neuralsignal over time. The coefficients of scales 3 to 6 are standardized perchannel, per scale, by subtracting out the mean and dividing by thestandard deviation of those scales and channels during training. Themean of the standardized coefficients is then taken for each channel.The resulting values are then used as features, called mean waveletpower (MWP), to be inputted to the decoder(s) running in real-time.

In some applications, a Fast Fourier Transform (FFT) may be used toextract features. Advantageously, an FFT may be used for obtaining powerinformation. Additionally, a nonlinear or linear transform may be use tomap the features to N-dimensional space (e.g. by use of a radial basisfunction). This may be useful when a desired movement can manifestitself in the form of multiple different electrical signals from theelectrodes, so that the system can recognize any of those differentsignals.

Next, the extracted features are sent to one or more decoders 106 thatassociate the features with a desired action, movement, thought, or soforth. It is contemplated that decoders can be implemented in at leasttwo different ways. First, as illustrated in FIG. 3A, the extractedfeatures may be sent as input to individual decoders 300. Each decoderhas previously been “trained” to associate certain features with aparticular movement. Examples of such decoded motions include:individual finger flex or extension; wrist flex or extension; radialdeviation; forearm supination or pronation; and hand open or close. Eachdecoder then outputs a value indicating a strength or confidence thatits particular movement has been identified. For example, each decodermay use a nonlinear kernel method (e.g. Gaussian radial basis) with anon-smooth support vector machine (SVM) that uses sparsity optimizationto zero out features that are least relevant to the motion beingidentified by that particular decoder. Advantageously, use of thedecoders allows for a determination of related, simultaneous movements.For example, the decoders may determine that a hand is closing witheither the arm moving or not moving. In addition, each decoder maydetermine a level of effort associated with its motion. Also, thedataset used by the decoder to determine its output may be based on onlya certain amount of history, so that the decoder adapts to changes inthe neural signal over time.

Alternatively, as illustrated in FIG. 3B, the decoders can be organizedin the form of a discrete multiclass decoder 310 that is used inparallel with a movement effort decoder 320. The discrete multiclassdecoder determines the motion(s) that is being imagined. For purposes ofthis disclosure, the multiclass decoder can be considered as a softwaremodule that receives the features as input, and can output any one ofmultiple desired movements. The movement effort decoder determines thelevel of movement effort of the identified desire movement(s). Anydecoded signal may be linearly or nonlinearly mapped to determine asignal delivered to the target. In general, the system can determineboth the user's movement intention before the physical movement begins,and also during movement as well. Again, it should be noted that theneural signals from the user are for movements that may be imagined orattempted, as well as actually performed.

Next, the output of the decoders is sent to a “body state observer” 108.The body state observer is a physics-based dynamic model of the body(e.g. including body parts such as arm, hand, leg, foot, etc.)implemented as software. The body state observer takes the desiredforces, torques or so forth as inputs and continuously updates andoutputs the velocity and position estimates of the actual body part(s).The body state observer can also accept data from body movement sensors110 as input. Such sensors may provide information on the position,velocity, acceleration, contraction, etc. of a body part. For example,sensors could provide information on the position of the elbow relativeto the shoulder and the wrist, and how these body parts are movingrelative to each other. In addition, the body state observer has a“memory” or a history of the feedback previously provided to the user.

The use of a body state observer is considered to have at least twoadvantages. First, the outputs of the body state observer can be used tocontinuously or dynamically change the stimulation patterns beingoutputted to the neurostimulation sleeve, to account for changedcircumstances. For example, if the stimulation electrodes aretranscutaneous, they can move with respect to their target muscles asthe joints move (e.g. pronation/supination). The stimulation patterngiven through the stimulation electrodes can thus be modified accordingto the relative shift between electrodes and muscles. In other words,the stimulation pattern may be dynamically changed (or held constant)based on a determined body state. This dynamic change in the stimulationpattern may be referred to as electrode movement compensation (EMC). Inone example of EMC, a stimulation pattern may be changed based onwhether a user's palm is up or down. Second, for transcutaneouselectrodes and even implanted electrodes, the force or torque at a givenstimulation current is often a function of joint position, velocity, orso forth. As the observer predicts joint position and velocity, thestimulation current can be adjusted accordingly to maintain a force ortorque desired by a user.

Examples of body states considered by the body state observer includepalm up or palm down; arm moving or not moving; a flexing, extension orcontraction of a wrist or other body part; and positions or movements ofjoints.

As another example, the body state observer can use the decoder outputsto estimate angular force at the joints corresponding to the desiredmotion using a model of the hand and forearm with 18 degrees of freedom.The model estimates the force caused by the contraction of the muscle,the force opposing the muscle contraction due to the anatomy of the handand forearm, a damping force, and the force of gravity. The output ofthe model is an estimate of the position of the hand and forearm inreal-time, taking into account the history of the stimulation providedto the forearm in order to estimate a current position of the hand andforearm.

Based on the output of the body state observer, the electricalstimulation pattern that is to be sent to the neurostimulation sleeve isdetermined by an encoding algorithm that generates the appropriatespatiotemporal patterns to evoke appropriate muscle contractions. Thepresent disclosure permits high definition stimulation. In highdefinition stimulation, the simulation pattern to the electrodes is“continuously” updated. For example, the stimulation pattern to theelectrodes is updated once every 0.1 seconds (e.g. 10 Hz). However,shorter and longer update times are also contemplated; in fact, speedsup to 50 Hz are contemplated. As discussed above, the simulation patternis provided based on the decoded motion(s) and is adjusted based on bodystates determined by the body state observer. To create a smoothermotion, a nonlinear or linear mapping may be applied to the output ofthe decoder(s). The user's particular user activation profile 114 canalso be used to modify or determine the electrical signal that is sentto the target. In this regard, different patients need a differentstimulation pattern to obtain the same movement of the target.Advantageously, this allows for delivery of a more effective stimulationpattern based on the individual characteristics of a user. Thestimulation pattern is then sent to the electrodes 118 to be transmittedto the muscles.

The stimulation pattern may be driven by the relative magnitude ofdecoder outputs. For example, the system generally has multiplesimultaneously running decoders for individual motions such as wristflexion, wrist extension; thumb flexion, thumb extension, etc. Eachdecoder has an output value (e.g. a value between −1 and +1), indicatingthe likelihood that the particular motion of that decoder is the desiredmovement, with −1 being unlikely and +1 being very likely. The decoderwith the greatest magnitude is taken to be the intended movement. Themagnitude of the decoder output can also used to drive the intensity ofthe stimulation, or the full stimulation intensity for the desiredmotion can be used.

Additionally, in high definition stimulation, multiple signal patternsmay be interleaved (e.g. by multiplexing) if more than one motion isdesired (e.g. a compound motion). For example, the stimulation patternneeded to lift the arm may be directed to different muscles than thosefor rotating the wrist. Interleaving permits multiple stimulationpatterns to be combined into a single stimulation signal sent to theneuromuscular sleeve, so that multiple movements can occur at the sametime. Again, this permits the body limb to move more naturally. Inaddition, advantageously, interleaving prevents electric field patternscreated by one stimulation pattern from interfering with electric fieldscreated by another stimulation pattern. This increases the number ofcomplex motions that the system is capable of. However, there is apractical limit to the number of stimulation patterns that may beinterleaved due to the fact that when the pulse rate for a singlesimulation pattern becomes too low (e.g. less than 10 pulses persecond), muscle twitches will start to become noticeable and movementsmoothness becomes undesirable. Still, interleaving is very effective,and allows for multiple movements to be performed simultaneously (e.g.in a compound movement). This could not be achieved with only a singlesimulation pattern.

In this regard, the system is able to make the following isolatedmotions: pinky flexion; pinky extension; ring flexion; middle flexion;middle extension; index flexion; index extension; thumb flexion; thumbextension; thumb abduction; thumb adduction; thumb opposition; wristflexion; wrist extension; wrist ulnar deviation; wrist radial deviation;forearm pronation; and forearm supination. These motions can be joinedtogether to create combined/compound movements. For example, all of thefingers are flexed at the same time to create a “hand close” motion.

In addition, motions may be sequenced. One example of this in a naturalsystem is a central pattern generator (CPG), which produces rhythmicpatterned outputs without sensory feedback. The software/systems of thepresent disclosure can mimic CPGs by producing a repeatable sequence ofevents, for example to return a targeted body part to an initial state.Another example of sequenced motions is a functional series of motions.Examples of functional series of motions include: teeth brushing,scratching, stirring a drink, flexing a thumb, cylindrical grasping,pinching, etc. These motions allow for manipulation of real-worldobjects of various sizes.

As a result of the stimulation 118, the target moves as desired by theuser. The electrical stimulation can be provided in the form ofcurrent-controlled, monophasic pulses of adjustable pulse rate, pulsewidth, and pulse amplitude which can be independently adjusted for eachchannel. For example, the pulse rate may be about 50 Hz, the pulse widthmay be about 500 microseconds (μsec), and the pulse amplitude can befrom 0 to about 20 milliamperes (mA), which can be changed every 100milliseconds. This cycle repeats continuously until the desired movementis achieved/attained. The stimulation signal/pattern sent to theelectrodes can be changed continuously through each cycle if needed, orcan be maintained, in order to obtain the desired movement. The softwarecan monitor either or both the neural activity and the motion of thetarget as detected by the sensors. It is noted that the neural signalmay not simply remain constant from the beginning of a desired movementuntil the end of a desired movement. Rather, for example, as an arm ismoving, the neural signal will change. This is because the body isproviding dynamic information to the brain on features such as theposition and velocity of the moving arm. The software can distinguishbetween these changes in neural activity based on time. Alternatively,the stimulation signal sent to the target may be actively changed due tochanges in the body state, for example due to the shift in the electrodeposition relative to their targeted muscle groups as a body limb moves.It is also contemplated that the user will be able to control bothparalyzed and non-paralyzed muscles simultaneously to achieve complextasks. Desirably, the decoder is also robust to context changes (such asarm position and speed) that are due to any stimulus that might changethe firing pattern/neural activity when the user thinks about a givenmotion. Some examples of context changes which might arise in the use ofthe system include: the user using an able-bodied limb while alsoreceiving stimulation to move an impaired limb; the user receivingdifferent types of tactile or visual feedback; or the user havingdifferent emotional states (attentive, excited, fatigued, etc)

In order to successfully operate, the software must be trained indecoding the neural signal to determine the desired movement. FIG. 4shows a flowchart related to such training. This flowchart is verysimilar to that of FIG. 1. Neural sensors 402 record a neural signalwhich is sent to lag minimization filters 404. The lag minimizationfilters 404 use a priori statistical information about the neural sensorsignals and use the least amount of history to filter the signals(minimizing lag and attenuation that fixed time window filters impose).In addition, during decoder training, a training feedback type selector408 operates in conjunction with training visual cues 410. The visualcue of operation/component 410 may be provided in the form of agraphical body part 510 (shown in FIG. 5). In this way, the user mayattempt or imagine a movement that the user is being shown, for example,by graphical body part 510, and then the user will produce neuralsignals that are useful in training a decoder 406 to identify themovement being shown. The dots for training feedback type selector 408and training visual cues 410 indicate that these boxes are used only fortraining, not during actual operation of the system.

In one related example of decoder training, a user may close his handwith his arm either moving or not moving while the decoders continuouslysearch for patterns. The decoder 406 receives inputs from both the lagminimization filters 404 and the training visual cues 410. In addition,during decoder training, the decoder 406 also receives an input from abody state observer 412, which is shown by dashed line 420. The bodystate observer itself receives input from body and/or neural sensors414. The decoder thus receives feedback that helps it to identify thesignals indicative of a particular movement. Stimulation is alsoprovided via stimulator 416 to electrodes 418, so that any change inneural activity due to the desired movement is reflected in the inputfrom the neural sensors 402.

Additionally, in decoder training, suitable machine learning techniquesmay be employed. For example, support vector machine (SVM) techniquesmay be used. During training, cues (e.g. visual, aural, etc.) may bepresented to the user while neural data is collected. This data iscollected for various positions over many different contexts in whichthe user might be thinking about a certain motion, to provide bettertraining data. For each point in time that a feature is calculated, thecorresponding cue may be recorded in the cue vector. In someembodiments, neural training data for both static movements and dynamicmovements can be combined (or ‘blended’) and used input to a nonlinearSVM. After training, a decoder may be built using the feature matrix andcue vector. A decoder is typically built by solving for weights matrix‘w’ and bias ‘Γ’ using suitable machine learning techniques to performclassification (e.g. discrete output) or regression (e.g. continuousoutput). Examples of methods that can be used to build the decoderinclude: Linear Discriminant Analysis, Least Squares Method, andDecision Trees such as Random Forest Type. The inputs and/or outputs tothe decoder can be filtered (e.g. smoothed over the time domain) as wellby using low-pass, high-pass, band-pass, Kalman, Weiner, etc. typefilters. Multiple decoders may be built (e.g. individual motion decoder,discrete multiclass decoder, movement effort decoder, etc.). Individualmovement decoders or a movement effort decoder can be used in real-timeto decode the intended force ‘F’ by using F=A*w−Γ where ‘A’ is thevector of features for the current point in time.

Additionally and advantageously, decoders are trained to recognize bothdiscrete type motions and rhythmic type motions. A discrete type motionis generally a single movement. In contrast, rhythmic type motionsinvolve multiple, repeatable “sub-motions.” Rhythmic type movements areused in daily activities such as teeth-brushing, cleaning/scrubbing,stirring liquids, and itching or scratching. Notably, when a userimagines a rhythmic movement, the user's neural activity pattern isdifferent than when the user is imagining a non-rhythmic movement, andidentifying these patterns sooner can help the resulting movements bemore natural.

In one example, three stimulation levels corresponding to a low, medium,and high evoked force response are provided to a user. Apiecewise-linear interpolation is used to construct a mapping fordecoder output to stimulation amplitudes for each motion. Duringreal-time decoding, this mapping allows for smooth physical transitionsfrom one movement to the next, controlled by the modulation ofintracortical signals. A single point calibration combined with linearinterpolation is insufficient due to the non-linear response of the armto electrical stimulation of varied amplitudes. At lower intracorticalmodulation levels, the stimulation produced would not be strong enoughto evoke the desired motion. The three point calibration facilitates theinitiation of the motion in the cases of lower modulation levels.

Referring now to FIG. 5, the display of a graphical body part can beadvantageously used for decoder training and real-time feedback to theuser. Here, a graphical body part 510 is displayed on a graphical userinterface (GUI) 550. The graphical body part 510 may be in the form of alarge hand 520 and/or a small hand 530. In one example, the small hand530 is used to show the user the desired movement. The user may thenattempt to perform the movement by thinking about it. The large hand 520provides feedback to the user on how well s/he is doing. For example,the small hand 530 can be controlled by an executable macro thatprovides cues to the user, while the large hand 520 is controlled by thebody state observer 108. In this way, the user is provided with bothvisual feedback (from the large hand 520) as well as electricalsimulation feedback (from the electrodes). The data collected is used to“train” the decoder for the particular movement. This is repeated formultiple different potential movements.

In some embodiments, while the user is attempting or imagining amovement cued by the GUI 550, electrical simulation may be applied tothe user in order to help evoke the cued movement. Therefore, theelectrical stimulation operates both to provide feedback to the user andevoke the cued movement.

Additionally, a movement depicted on GUI 550 may be a rhythmic typemovement (in contrast to a discrete type movement). Rhythmic typemovements are used in daily activities such as teeth-brushing,cleaning/scrubbing, stirring liquids, and itching or scratching.

To test the decoders, the user is asked to imagine the movements again,and the system is used to decode their thoughts in real time. Whilecontinuously decoding, the system also stimulates the electrodes on thelimb to evoke the decoded movement, providing feedback to the user.

The GUI 550 also allows a user to shape the average current duringsimulation of a given electrode pattern. For example, if it is desiredto cause a forearm to be pronated, a user may select a stimulationpattern with average current decays to 0 mA over the course of 1.5seconds with the decay occurring proportional to cos(t²). Thestimulation may be updated, for example, at a rate of 10 updates persecond. An operator of the GUI 550 may use the GUI 550 to select, forexample, simulation signal amplitude or shape, a spatial configurationof the electrodes, simulation patterns in a sequence (e.g. to test asequence of motions), or so forth. The GUI 550 may also indicate adeflection or force of the user (e.g. firm or weak grip).

FIG. 6 shows an illustrative example of the methods described herein. Inoperation 600, a first measurement of neural activity is taken. Inoperation 602, based on the first measurement of neural activity, adesired movement of a target is determined. In operation 604, anelectrical signal is delivered to the target to start movement of thetarget, and a second neural activity of the user is measured. Inoperation 606, based on the second neural activity of the user, thesignal being delivered to the target is maintained or changed. Thiscycle repeats itself continuously, so that the desired movement isachieved in real time. Unexpectedly, it was found that during even largechanges in neural activity, the electrical signal delivered to thetarget should sometimes be maintained rather than changed to produce adesired movement.

It will further be appreciated that the disclosed techniques may beembodied as a non-transitory storage medium storing instructionsreadable and executable by a computer, (microprocessor ormicrocontroller of an) embedded system, or various combinations thereof.The non-transitory storage medium may, for example, comprise a hard diskdrive, RAID or the like of a computer; an electronic, magnetic, optical,or other memory of an embedded system, or so forth.

The processes and systems of the present disclosure are illustrated inthe following non-limiting examples.

EXAMPLES Example 1

FIGS. 7A-18B are pictures taken from a set of experiments where anable-bodied user's left hand was wrapped with a neuromuscular electricalstimulation sleeve. The user was asked to move his hand into differentpositions. In each set, one picture shows an image of the hand/armmotion that the user performed with his limb, and the other pictureshows a graphical hand representation of the user's limb position basedon position sensor data collected from the sleeve. In each set ofpictures, the actual limb moved is shown on the left hand (the Apicture), and the graphical limb moved is shown on the right hand (the Bpicture). This was due to the setup of the system. As seen here, thecorrespondence between the measured position shown by the graphical limband the actual limb movement corresponded very well.

FIGS. 19A-19C are bar graphs showing the different percentages ofcurvature for each digit during finger extension, finger fair curvature,and making a fist. As seen in FIG. 19A, the digits were generallystraight for finger extension, with only three digits having anycurvature, and all of them being less than 20% curvature. In FIG. 19B,all of the digits were curved about halfway during finger faircurvature. In FIG. 19C, all of the digits were curved near theirmaximum, as desired. These illustrate the accuracy of the positionsensors and the software that processes their output.

Example 2

Cortical control of rhythmic movements which originate in the brain butare coordinated by Central Pattern Generator (CPG) neural networks inthe spinal cord has not been demonstrated previously. Experiments wereconducted on a patient with a paralyzed limb to demonstrate that thepresent system could decode cortical activity and emulate spinal cordCPG function allowing volitional rhythmic hand movement. The technologyused a combination of signals recorded from the brain, machine-learningalgorithms to decode the signals, a numerical model of CPG network, anda neuromuscular electrical stimulation system to evoke rhythmicmovements. Using the neural bypass, a quadriplegic participant was ableto initiate, sustain, and switch between rhythmic and discrete fingermovements, using his thoughts alone.

Many rhythmic activities, such as breathing, walking/running, stirring,teeth-brushing, scratching, or playing a musical instrument, areinitiated in the brain but also require further downstream coordinationin the spinal cord. Networks of neurons in the spinal cord known asCentral Pattern Generators (CPGs) are responsible for producing theserhythmic activities. CPGs integrate input signal from the brain withoutsensory feedback from the limbs to produce rhythmic movements.

The results showed that: 1) stable rhythmic oscillations can begenerated using a virtual CPG oscillator, 2) neural activity in themotor cortex is distinct for imagining rhythmic movements compared todiscrete movements, 3) neural decoders can be trained to successfullyclassify imagined rhythmic versus discrete movements, and 4) the decodedneural activity in combination with the CPG oscillator and NMES candifferentially enable rhythmic versus discrete movements in a paralyzedfinger.

Setup

Initially, the CPG oscillator model used to generateoscillatory/rhythmic patterns was based on the biologically inspiredtwo-neuron oscillator as described by Matsuoka (Biol. Cybern., 1985,52(6): pp. 367-76) in which two neurons (the extensor neuron and theflexor neuron) are interconnected in a mutually inhibitive network asillustrated in FIG. 20.

The CPG oscillator is capable of generating oscillatory output, theamplitude and frequency of which can be independently controlled bytuning the parameters. The amplitude is positively correlated to thetonic input c. The frequency is positively correlated to the adaptationcoefficient β and negatively correlated to the time constants τ1 and τ2.The v represents the membrane current, μ the weight of the interactionbetween the neurons, y is the neuronal output. Parameters were selectedto generate stable oscillations at 2.5 Hz. This frequency was selecteddue to hardware and software limitations as the rate at which theneuromuscular stimulator could be updated was limited to 10 updates/sec,and the muscles required approximately 0.2 sec of continuous stimulationof the stimulation pattern in order to achieve measurable deflection ofthe thumb. Similarly the output amplitude of the CPG oscillator wasdiscretized, which was used to trigger the maximum flexion/extensionstimulation of the thumb in order to achieve the maximum deflection thatwas physically possible for the thumb.

FIG. 21 shows the test setup for measuring hand motion. To quantifythumb movements and measure performance, colored finger cots were placedon the participant's hand during testing. For added sensitivity indetecting movements involving the wrist (used as reference), anadditional cot was placed on a plastic cylinder extending out past theparticipant's thumb, to act as a “sixth finger”. A Bumblebee®2 stereocamera (Richmond, Canada) was positioned above the participant's hand totrack movement in three dimensions. The color of the cots was used toidentify the thumb and locate it in three-dimensional space using acombination of custom code and OpenCV. The location of the thumbrelative to the sixth finger was used to determine thumb movement.

Next, the neural decoder (i.e. the software) was trained for a givenmovement by asking the participant to imagine mimicking hand movementscued to him by an animated virtual hand on a computer monitor. The cuedmovements corresponded to discrete thumb extension, discrete thumbflexion, and rhythmic thumb wiggle. Each movement cue was directlyfollowed by a cue to rest and the duration of the movement and rest cueswas randomly selected between 2.5 sec and 4.5 sec. The ordering of themovement cues was randomly shuffled so the participant could notanticipate the next cue. The neural decoders were trained in trainingblocks, each consisting of multiple repetitions of each desired motion.This full set of data was used as input for training a nonlinear SupportVector Machine (SVM) algorithm, to generate a robust set of decoders. Adecoder for each motion (against all other motions and rest) was builtusing a nonlinear Gaussian radial basis function kernel to process thisfull set of data and a non-smooth SVM algorithm that uses sparsityoptimization to improve performance.

During the test period, all decoders ran simultaneously and the decoderwith the highest output score above zero was used to drive electricalstimulation. The output score of each movement decoder is between −1 and+1. When the output score of a movement decoder exceeded zero, thesystem enabled stimulation for that movement. If the output scores ofmultiple movement decoders exceeded zero simultaneously, then the systemenabled the movement with the highest decoder score.

Neuromuscular electrical stimulation (NMES) was provided through the useof a custom-made high-definition sleeve. Briefly, the desired movementwas evoked by the stimulator providing electrical stimulation throughthe electrodes on a sleeve wrapped around the participant's forearm. Thesleeve contains 130 electrodes and hydrogel disks of 12 mm diameter. Thecenter-to-center spacing of the electrodes is 22 mm along the long axisof the forearm and 15 mm in the transverse direction. Electricalstimulation was provided in the form of current-controlled, monophasicrectangular pulses of 50 Hz pulse rate and 500 μs pulse width. For eachmovement, a stimulator calibration was performed to determine theappropriate spatial electrode pattern by using a trial-and-error method.

Experiments and Results Decoded cortical signals were then used to drivethe virtual CPG oscillator which in turn controlled the NMES tostimulate the paralyzed muscles and generate movements, therebybypassing the injured spinal cord. Motor cortical neural activity wascontinuously recorded as the participant was cued to imagine one of thethree trained movements (discrete thumb flexion, discrete thumbextension, and rhythmic thumb wiggle) interleaved with rest periods.Cues were delivered by an animated virtual hand on the computer monitor.

FIGS. 22A-22C show the raster histograms from one discriminated unit(channel 7 unit 1) with response to attempted thumb movements and thecorresponding wavelet processed neural data showing average mean waveletpower (MWP).

FIG. 23A shows representative heat maps of the spatial distribution ofthe neural activity for the three imagined movements as represented bythe normalized MWP overlaid on the physical layout of the array. FIG.23B shows the correlation coefficient between the cue and the MWP oneach channel for each motion (FIG. 3b ). The correlation wassignificantly different (p<0.05) on 79 out of 96 channels when theparticipant imagined the rhythmic thumb wiggle compared to when heimagined thumb flexion and on 62 out of 96 channels for imaginedrhythmic thumb wiggle compared to imagined thumb extension. The MWP onsome channels was not only highly correlated (correlationcoefficient>0.3) for rhythmic movement, but was also negativelycorrelated with either the discrete flexion or extension movement. FIG.23C shows the modulation in MWP on representative channel #7, 70 and 87for the three imagined movements, showing distinct modulation forimagined thumb wiggle compared to imagined thumb extension and flexion.

Next, to investigate if the cortically-controlled NMES system coulddifferentially control both discrete flexion/extension movements andmore complex, rhythmic digit movements, the system's ability to evokethese movements on cue was tested. Test blocks were performed consistingof six trials of each of the three trained movements presented in randomorder with a rest period between each movement. Cortical activity wascontinuously decoded using the trained decoders as the participantattempted either the cued movement or rest, as described above. Neuraldecoder output corresponding to the rhythmic thumb wiggle was used totrigger the virtual CPG oscillator, which used the identified parametersto generate the oscillatory output used by the NMES for real-timestimulation of alternating extensor and flexor muscles in theparticipant's forearm resulting in a rhythmic thumb wiggle. Neuraldecoder output classified as either a discrete flexion or discreteextension was fed to the NMES for realtime stimulation of musclescontrolling either thumb flexion or extension respectively.

Representative data, including modulation of MWP (before and afterstimulation begins), decoder outputs, and corresponding movement stateare shown in FIG. 24A for thumb wiggle, in FIG. 24B for thumb flexion,and FIG. 24C for thumb extension. MWP increases after stimulation beginsdue to residual stimulation artifact. However, since the neural decoderswere trained with MWP data from before and during stimulation, theneural decoders were not only able to recognize the correct imaginedmovement to initiate stimulation but also the participant's desire tosustain and subsequently terminate the target movement. The participantwas able to successfully initiate, sustain, and complete distinctflexion, extension and wiggle movements through his thoughts alone.

Accuracy was measured by an automated computer-based evaluation of videoframes of thumb movements as the fraction of frames that matched thecued movement. Sensitivity was defined as the proportion of movementcues that were identified correctly, and specificity was defined as theproportion of rest cues that were identified correctly. The resultsobtained are laid out in the table below:

Thumb Thumb Thumb Movement Wiggle Extension Flexion Accuracy 91.5 ± 0.6%90.9 ± 0.6% 84.6 ± 0.8% Sensitivity 96.4 ± 1.0% 62.1 ± 2.6% 94.0 ± 1.3%Specificity 90.6 ± 0.7% 96.4 ± 0.4% 82.9 ± 0.8%

Using the system, the participant achieved an overall accuracy of77.4%±0.9% (mean s.d., p<0.01) across all states of rest, wiggle,extension and flexion. The overall accuracy is lower than the individualaccuracies because it accumulates the errors for each of the threeindividual movements.

The present disclosure has been described with reference to exemplaryembodiments. Obviously, modifications and alterations will occur toothers upon reading and understanding the preceding detaileddescription. It is intended that the present disclosure be construed asincluding all such modifications and alterations insofar as they comewithin the scope of the appended claims or the equivalents thereof.

1-20. (canceled)
 21. A method of creating a neural bridge, comprising: measuring brain neural activity of a user; based on the measured brain neural activity, determining a desired compound movement comprising desired movements of multiple joints of a target; identifying stimulation patterns to the multiple joints that do not interfere with each other and will result in the desired compound movement; and delivering an electrical signal to the target to produce the desired compound movement of the target; wherein the delivering of the electrical signal to the target includes interleaving delivery of the identified stimulation patterns to the multiple joints.
 22. The method of claim 21 wherein the interleaving of the delivery of the identified stimulation patterns to the multiple joints delivers at least 10 pulses per second for each stimulation pattern of the identified stimulation patterns.
 23. The method of claim 21 wherein: the target comprises a hand of the user; and the desired movements of the multiple joints of the target include two or more of: a pinky flexion movement; a pinky extension movement; a ring flexion movement; a middle flexion movement; a middle extension movement; an index flexion movement; an index extension movement; a thumb flexion movement; a thumb extension movement; a thumb abduction movement; a thumb adduction movement; a thumb opposition movement; a wrist flexion movement; a wrist extension movement; a wrist ulnar deviation movement; a wrist radial deviation movement; a forearm pronation movement; and a forearm supination movement.
 24. The method of claim 21 wherein the target includes a hand of the user and the compound movement includes at least a hand close movement.
 25. The method of claim 24 wherein the target further includes an arm of the user and the compound movement includes at least the hand close movement with a movement of the arm.
 26. The method of claim 21 wherein the target includes a hand of the user, the desired movements of the multiple joints of the target include joints of the hand, and the compound movement includes at least a hand open movement.
 27. The method of claim 21 wherein the target is a body limb or a prosthetic limb, an exoskeleton, or an external robotic arm.
 28. The method of claim 21 wherein the determining of the desired compound movement includes: extracting features from the measured brain neural activity; and inputting the extracted features to a discrete multiclass decoder to identify whether a particular movement is desired.
 29. The method of claim 21 wherein the brain neural activity is measured using a set of electrodes, and the method further comprises: tracking a state of the target during the delivering of the electrical signal to the target; and performing electrode movement compensation (EMC) based on the tracked state of the target.
 30. The method of claim 21 wherein the target is a body part of the user and the system further comprises: transcutaneous electrodes configured to contact a surface of skin of the body part; wherein the electronic data processing device is programmed to deliver the electrical signal the body limb to produce the desired compound movement of the body part via the transcutaneous electrodes.
 31. A neural bridge system comprising: electrodes configured to measure brain neural activity of a user; a target having a plurality of joints; and an electronic data processing device programmed to perform operations including: based on the measured brain neural activity, determining a desired compound movement comprising desired movements of multiple joints of the target; identifying stimulation patterns to the multiple joints that do not interfere with each other and will result in the desired compound movement; and delivering an electrical signal the target to produce the desired compound movement of the target, wherein the delivering of the electrical signal to the target includes interleaving delivery of the identified stimulation patterns to the multiple joints.
 32. The system of claim 31 wherein the interleaving of the delivery of the identified stimulation patterns to the multiple joints delivers at least 10 pulses per second for each stimulation pattern of the identified stimulation patterns.
 33. The system of claim 31 wherein: The target includes a hand of the user; and the desired movements of the multiple joints of the target include two or more of: a pinky flexion movement; a pinky extension movement; a ring flexion movement; a middle flexion movement; a middle extension movement; an index flexion movement; an index extension movement; a thumb flexion movement; a thumb extension movement; a thumb abduction movement; a thumb adduction movement; a thumb opposition movement; a wrist flexion movement; a wrist extension movement; a wrist ulnar deviation movement; a wrist radial deviation movement; a forearm pronation movement; and a forearm supination movement.
 34. The system of claim 31 wherein the target includes a hand of the user and the compound movement includes at least a hand close movement.
 35. The system of claim 34 wherein the target further includes an arm of the user and the compound movement includes at least the hand close movement with a movement of the arm.
 36. The system of claim 31 wherein the target includes a hand of the user and the compound movement includes at least a hand open movement.
 37. The system of claim 31 wherein the determining of the desired compound movement includes: extracting features from the measured brain neural activity; and inputting the extracted features to a discrete multiclass decoder to identify whether a particular movement is desired.
 38. The method of claim 31 wherein the brain neural activity is measured using a set of electrodes, and the method further comprises: tracking a state of the target during the delivering of the electrical signal to the target; and performing electrode movement compensation (EMC) based on the tracked state of the target.
 39. The system of claim 31 wherein the target is a body part of the user and the system further comprises: transcutaneous electrodes configured to contact a surface of skin of the body part; wherein the electronic data processing device is programmed to deliver the electrical signal the target to produce the desired compound movement of the target via the transcutaneous electrodes.
 40. A non-transitory storage medium storing instructions readable and executable by an electronic data processing device to create a neural bridge by operations including: receiving brain neural activity of a user via a set of electrodes; based on the measured brain neural activity, determining a desired compound movement comprising desired movements of multiple joints of a target; identifying stimulation patterns to the multiple joints that do not interfere with each other and will result in the desired compound movement; and delivering an electrical signal to the target to produce the desired compound movement of the target; wherein the delivering of the electrical signal to the target includes interleaving delivery of the identified stimulation patterns to the multiple joints. 